pub struct RLOFOpticalFlowParameter { /* private fields */ }
Expand description

This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm.

The RLOF is a fast local optical flow approach described in Senst2012 Senst2013 Senst2014 and Senst2016 similar to the pyramidal iterative Lucas-Kanade method as proposed by Bouguet00. More details and experiments can be found in the following thesis Senst2019. The implementation is derived from optflow::calcOpticalFlowPyrLK(). This RLOF implementation can be seen as an improved pyramidal iterative Lucas-Kanade and includes a set of improving modules. The main improvements in respect to the pyramidal iterative Lucas-Kanade are:

  • A more robust redecending M-estimator framework (see Senst2012) to improve the accuracy at motion boundaries and appearing and disappearing pixels.
  • an adaptive support region strategies to improve the accuracy at motion boundaries to reduce the corona effect, i.e oversmoothing of the PLK at motion/object boundaries. The cross-based segementation strategy (SR_CROSS) proposed in Senst2014 uses a simple segmenation approach to obtain the optimal shape of the support region.
  • To deal with illumination changes (outdoor sequences and shadow) the intensity constancy assumption based optical flow equation has been adopt with the Gennert and Negahdaripour illumination model (see Senst2016). This model can be switched on/off with the useIlluminationModel variable.
  • By using a global motion prior initialization (see Senst2016) of the iterative refinement the accuracy could be significantly improved for large displacements. This initialization can be switched on and of with useGlobalMotionPrior variable.

The RLOF can be computed with the SparseOpticalFlow class or function interface to track a set of features or with the DenseOpticalFlow class or function interface to compute dense optical flow.

See also

optflow::DenseRLOFOpticalFlow, optflow::calcOpticalFlowDenseRLOF(), optflow::SparseRLOFOpticalFlow, optflow::calcOpticalFlowSparseRLOF()

Implementations

Creates instance of optflow::RLOFOpticalFlowParameter

Trait Implementations

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Enable M-estimator or disable and use least-square estimator. Enables M-estimator by setting sigma parameters to (3.2, 7.0). Disabling M-estimator can reduce * runtime, while enabling can improve the accuracy. Read more

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